Hybrid Poissoflolynomial Objective Functions for Tomographic Image Reconstruction from Transmission Scans
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.date.accessioned | 2011-08-18T18:21:23Z | |
dc.date.available | 2011-08-18T18:21:23Z | |
dc.date.issued | 1995-10 | en_US |
dc.identifier.citation | Fessler, Jeffrey A. (1995). "Hybrid Poissoflolynomial Objective Functions for Tomographic Image Reconstruction from Transmission Scans". IEEE Transactions on Image Processing 4(10): 1439-1450. <http://hdl.handle.net/2027.42/86023> | en_US |
dc.identifier.issn | 1057-7149 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/86023 | |
dc.description.abstract | This paper describes rapidly converging algorithms for computing attenuation maps from Poisson transmission measurements using penalized-likelihood objective functions. We demonstrate that an under-relaxed cyclic coordinate-ascent algorithm converges faster than the convex algorithm of Lange (see ibid., vol.4, no.10, p.1430-1438, 1995), which in turn converges faster than the expectation-maximization (EM) algorithm for transmission tomography. To further reduce computation, one could replace the log-likelihood objective with a quadratic approximation. However, we show with simulations and analysis that the quadratic objective function leads to biased estimates for low-count measurements. Therefore we introduce hybrid Poisson/polynomial objective functions that use the exact Poisson log-likelihood for detector measurements with low counts, but use computationally efficient quadratic or cubic approximations for the high-count detector measurements. We demonstrate that the hybrid objective functions reduce computation time without increasing estimation bias. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Hybrid Poissoflolynomial Objective Functions for Tomographic Image Reconstruction from Transmission Scans | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.identifier.pmid | 18291975 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/86023/1/Fessler100.pdf | |
dc.identifier.doi | 10.1109/83.465108 | en_US |
dc.identifier.source | IEEE Transactions on Image Processing | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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